This course introduces students to research designs commonly used by social scientists to study people, organizations, and markets. To demonstrate empirical techniques, approaches from various fields and contexts are used throughout the course.
Students learn to formulate interesting research questions, construct novel theoretical arguments, derive testable hypotheses, choose appropriate methods, design empirical studies, and use data visualization and econometrics to construct appealing “numerical narratives” for scholarly audiences. We discuss the importance of qualitative methods in the context of choosing an appropriate method, but focus primarily on quantitative methods. We will also emphasize the visual and intuitive presentation of empirical results obtained from large-scale data analysis.
This course is suitable for students conducting research within a variety of management and related social science disciplines (e.g., Economics, Political Science, Psychology, Sociology). It is suitable for all doctoral students, regardless of their stage in the program or area of study.
Relative to statistics courses, this course focuses more on developing and testing arguments and on applying proper research methods. Data and basic programming techniques will illustrate the intuition of various methods; we will not emphasize formulas, equations, proofs, or mathematical modeling.
intended learning outcomes
Formulate interesting and tractable research questions.
- Develop novel and coherent theoretical accounts of observed empirical phenomena.
- Articulate fundamental assumptions, theoretical mechanisms, and testable hypotheses.
- Evaluate empirical contexts for their potential to establish facts and to reduce ignorance about phenomena.
- Design empirical research projects likely to yield credible inferences about phenomena.
- Construct compelling numerical narratives that relate theory to empirical observation with descriptive statistics and data visualization.
Intermediate assignments guide students toward the final deliverable. Each assignment is an opportunity for the instructor and/or peer feedback on a student’s research design. Not all assignments are submitted to the instructor; some are discussed in class.
At the end of the course, each student will prepare a paper of up to 10 pages or a 15-20 presentation of an empirical research design for answering their research question(s). Students will be graded on a Pass, Fail, or Fail w/Revision basis.
Final reading list to be published before course start.
Lave, C. A. & J. G. March. 1993(1975). “An introduction to speculation.” Chapter 2 in An Introduction to Models in the Social Sciences. New York: University Press of America.
Merton, R. K. (1987). “Three Fragments From a Sociologist’s Notebooks: Establishing the Phenomenon, Specified Ignorance, and Strategic Research Materials.” Annual Review of Sociology, 13(1): 1-29. [Read pp. 1-19]
Elster, J. (2007). “Explanation.” Chapter 1, pp. 9-30 in Explaining social behavior: More nuts and bolts for the social sciences. Cambridge University Press: New York.
Hedström, P. & K. Wennberg. (2017) “Causal mechanisms in organization and innovation studies.” Innovation, 19:1, 91-102.
Damodaran, A. (2017). Pp. 10-51 in Narrative and Numbers: The Value of Stories in Business. Columbia Business School Publishing: New York.
Healy, K. & J. Moody. (2018). “Data visualization in sociology.” Annual Review of Sociology, 40: 105-128.
Angrist, J. and J. S. Pischke. (2009). “Questions about questions.” Pp. 3-9 in Mostly Harmless Econometrics: An Empiricist’s Companion.
Angrist, J. and J. S. Pischke. (2009). “The experimental ideal.” Pp. 9-18 in Mostly Harmless Econometrics: An Empiricist’s Companion.
Angrist, J. and J. S. Pischke. (2009). “Making regression make sense.” Pp. 19-50 in Mostly Harmless Econometrics: An Empiricist’s Companion.
Rider, C. I. (2021). “Numerical Narrative.” Tips on website.
Rider, C. I. (2021). “Empirical Etiquette.” Tips on website.
Course set up:
The course will be given in the SSES office as well as digitally.